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Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models

This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.

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Hovedforfatter: Scheubner, Stefan
Format: Online
Sprog:engelsk
Udgivet: KIT Scientific Publishing 2022
Fag:
Elektromobilität
Vorhersagen
Algorithmen
Fahrzeugtechnik
Energiemanagement
E-Mobility
Forecasting
Algorithms
Vehicle Technology
Energy Management
Online adgang:ONIX_20220620_9783731511663_74
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Summary:This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.

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